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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.
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Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.
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This is a review article invited by Atmosphere-Ocean to document the contributions of Recherche en Prévision Numérique (RPN) to Numerical Weather Prediction (NWP). It is structured as a historical review and documents RPN’s contributions to numerical methods, numerical modelling, data assimilation, and ensemble systems, with a look ahead to potential future systems. Through this review, we highlight the evolution of RPN’s contributions. We begin with early NWP efforts and continue through to environmental predictions with a broad range of applications. This synthesis is intended to be a helpful reference, consolidating developments and generating broader interest for future work on NWP in Canada.