The role and impact that boundary layer and shallow cumulus clouds have on the medium-range forecast of a large-scale weather system is discussed in this study. A mesoscale version of the Global Environmental Multiscale (GEM) atmospheric model is used to produce a 5-day numerical forecast of a midlatitude large-scale weather system that occurred over the Pacific Ocean during February 2003. In this version of GEM, four different schemes are used to represent (i) boundary layer clouds (including stratus, stratocumulus, and small cumulus clouds), (ii) shallow cumulus clouds (overshooting cumulus), (iii) deep convection, and (iv) nonconvective clouds. Two of these schemes, that is, the so-called MoisTKE and Kuo Transient schemes for boundary layer and overshooting cumulus clouds, respectively, have been recently introduced in GEM and are described in more detail. The results show that GEM, with this new cloud package, is able to represent the wide variety of clouds observed in association with the large-scale weather system. In particular, it is found that the Kuo Transient scheme is mostly responsible for the shallow/intermediate cumulus clouds in the rear portion of the large-scale system, whereas MoisTKE produces the low-level stratocumulus clouds ahead of the system. Several diagnostics for the rear portion of the system reveal that the role of MoisTKE is mainly to increase the vertical transport (diffusion) associated with the boundary layer clouds, while Kuo Transient is acting in a manner more consistent with convective stabilization. As a consequence, MoisTKE is not able to remove the low-level shallow cloud layer that is incorrectly produced by the GEM nonconvective condensation scheme. Kuo Transient, in contrast, led to a significant reduction of these nonconvective clouds, in better agreement with satellite observations. This improved representation of stratocumulus and cumulus clouds does not have a large impact on the overall sea level pressure patterns of the large-scale weather system. Precipitation in the rear portion of the system, however, is found to be smoother when MoisTKE is used, and significantly less when the Kuo Transient scheme is switched on.

The summertime improvement resulting from the operational implementation of a new surface modeling and assimilation strategy into the Canadian regional weather forecasting system is described in this study. The surface processes over land are represented in this system using the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme. Surface variables, including soil moisture, are initialized using a sequential assimilation technique in which model errors of low-level air temperature and relative humidity are used to determine analysis increments of surface variables. It was found that the magnitude and nature of the analysis increments applied to the surface variables depended on the surface and meteorological conditions observed in each region. In regions characterized by weak meteorological activity (i.e., no clouds or precipitation), model errors of low-level air characteristics are more likely to be related to an incorrect representation of surface processes due to either ... Abstract The summertime improvement resulting from the operational implementation of a new surface modeling and assimilation strategy into the Canadian regional weather forecasting system is described in this study. The surface processes over land are represented in this system using the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme. Surface variables, including soil moisture, are initialized using a sequential assimilation technique in which model errors of low-level air temperature and relative humidity are used to determine analysis increments of surface variables. It was found that the magnitude and nature of the analysis increments applied to the surface variables depended on the surface and meteorological conditions observed in each region. In regions characterized by weak meteorological activity (i.e., no clouds or precipitation), model errors of low-level air characteristics are more likely to be related to an incorrect representation of surface processes due to either ...

The Meteorological Service of Canada (MSC) recently implemented a 33-km version of the Global Environmental Multiscale (GEM) model, with improved physics, for medium-range weather forecasts. Quantitative precipitation forecasts (QPFs) from this new system were compared with those from the previous global operational system (100-km grid size) and with those from MSC’s short-range (48 h) regional system (15-km grid size). The evaluation is based on performance measures that evaluate bias, accuracy, and the value of the QPFs. Results presented in this article consistently show, for these three aspects of the evaluation, that the new global forecast system (GLBNEW) agrees more closely with observations, relative to the performance of the previous global system (GLBOLD). The biases are noticeably smaller with GLBNEW compared with GLBOLD, which severely overpredicts (underpredicts) the frequencies and total amounts associated with weak (strong) precipitation intensities. The accuracy and value scores reveal gains of at least 12 h and even up to 72 h for medium-range QPFs (i.e., day 3 to day 5 predictions). The new global system even performs slightly better than MSC’s operational regional 15-km system for short-range QPFs. In a more absolute manner, results suggest that QPFs from the new global system may still have accuracy and value even at the medium range. This seems to be true at least for the smallest precipitation threshold, related to precipitation occurrence, for which the positive area under curves of relative economic value remains important, even for day 5 QPFs.

In this study, the ability of a turbulent kinetic energy (TKE)–based boundary layer scheme to reproduce the rapid evolution of the planetary boundary layer (PBL) observed during two clear convective days is examined together with the impact of including nonlocal features in the boundary layer scheme. The two cases are chosen from the Montreal-96 Experiment on Regional Mixing and Ozone (MERMOZ): one is characterized by strong buoyancy, a strong capping inversion, and weak vertical wind shear; the other displays moderate buoyancy, a weaker subsidence inversion, and significant wind shear near the PBL top. With the original local version of the turbulence scheme, the model reproduces the vertical structures and turbulent quantities observed in the well-developed boundary layer for the first case. For the second case, the model fails to reproduce the rapid evolution of the boundary layer even though the TKE and sensible heat fluxes are greatly overpredicted. Some nonlocal aspects of the turbulence scheme are tested for these two cases. Inclusion of nonlocal (countergradient) terms in the vertical diffusivity equation has little impact on the simulated PBL. In contrast, alternative formulations of the turbulent length scales that follow the strategy proposed by Bougeault and Lacarrère have a greater influence. With the new turbulent lengths, entrainment at the top of the boundary layer is enhanced so that the depth of the well-mixed layer is much larger compared to that of the local simulations even though the turbulent sensible heat fluxes are smaller. Comparison with observations reveals, however, that the inclusion of these modifications does not improve all aspects of the simulation. To improve the performance and reduce somewhat the arbitrariness in the Bougeault–Lacarrère technique, a relationship between the two turbulent length scales (mixing and dissipation) used in the turbulence scheme is proposed. It is shown that, in addition to reducing the sensitivity of the results to the particular formulations, the simulated boundary layer agrees better with observations.

This proof‐of‐concept study couples machine learning and physical modeling paradigms to develop a computationally efficient simulator‐emulator framework for generating super‐resolution (<250 m) urban climate information, that is required by many sectors. To this end, a regional climate model/simulator is applied over the city of Montreal, for the summers of 2019 and 2020, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super‐resolution deep learning (DL) model/emulator. The DL model uses an efficient sub‐pixel convolution layer to generate HR information from LR data, with adversarial training applied to improve physical consistency. The DL model reduces temperature errors significantly over urbanized areas present in the LR simulation, while also demonstrating considerable skill in capturing the magnitude and location of heat stress indicators. These results portray the value of the innovative simulator‐emulator framework, that can be extended to other seasons/periods, variables and regions.

Governments around the world have implemented measures to slow down the spread of COVID-19, resulting in a substantial decrease in the usage of motorized transportation. The ensuing decrease in the emission of traffic-related heat and pollutants is expected to impact the environment through various pathways, especially near urban areas, where there is a higher concentration of traffic. In this study, we perform high-resolution urban climate simulations to assess the direct impact of the decrease in traffic-related heat emissions due to COVID-19 on urban temperature characteristics. One simulation spans the January–May 2020 period; two additional simulations spanning the April 2019–May 2020 period, with normal and reduced traffic, are used to assess the impacts throughout the year. These simulations are performed for the city of Montreal, the second largest urban centre in Canada. The mechanisms and main findings of this study are likely to be applicable to most large urban centres around the globe. The results show that an 80% reduction in traffic results in a decrease of up to 1 °C in the near-surface temperature for regions with heavy traffic. The magnitude of the temperature decrease varies substantially with the diurnal traffic cycle and also from day to day, being greatest when the near-surface wind speeds are low and there is a temperature inversion in the surface layer. This reduction in near-surface temperature is reflected by an up to 20% reduction in hot hours (when temperature exceeds 30 °C) during the warm season, thus reducing heat stress for vulnerable populations. No substantial changes occur outside of traffic corridors, indicating that potential reductions in traffic would need to be supplemented by additional measures to reduce urban temperatures and associated heat stress, especially in a warming climate, to ensure human health and well-being.