Physics-informed deep learning framework to model intense precipitation events at super resolution.

Physical modeling of precipitation at fine (sub-kilometer) spatial scales is computationally very expensive. This study develops a highly efficient framework for this task by coupling deep learning (DL) and physical modeling. This framework is developed and tested using regional climate simulations performed over a domain covering Montreal and adjoining regions, for the summers of 2015-2020, at 2.5 km and 250 m resolutions. The DL framework uses a recurrent approach and considers atmospheric physical processes, such as advection, to generate high-resolution information from low-resolution data, which enables it to recreate fine details and produce temporally consistent fields. The DL framework generates realistic high-resolution precipitation estimates, including intense short-duration precipitation events, which allows it to be applied in engineering problems, such as evaluating the climate resiliency of urban storm drainage systems. The results portray the value of the proposed DL framework, which can be extended to other resolutions, periods, and regions.

[Elaboration and validation of a tool to measure psychological well-being: WBMMS]. / Elaboration et validation d'un outil de mesure du bien-être psychologique: l'EMMBEP.

Psychological well-being scales used in epidemiologic surveys usually show high construct validity. The content validation, however, is less convincing since these scales rest on lists of items that reflect the theoretical model of the authors. In this study we present results of the construct and criterion validation of a new Well-Being Manifestations Measure Scale (WBMMS) founded on an initial list of manifestations derived from an original content validation in a general population. It is concluded that national and public health epidemiologic surveys should include both measures of positive and negative mental health.

[Evaluation and validation of a test of psychological distress in a general population in french Quebec]. / Elaboration et validation d'un outil de mesure de la détresse psychologique dans une population non clinique de Québécois francophones.

Psychological distress scales used in epidemiologic surveys usually show high construct validity. The content validation however is less convincing since these scales rest on lists of psychiatric symptoms which are hypothesized as the very content of a psychological distress. This study presents the results of the construct and criterion validation of a new Psychological Distress Manifestations Measure Scale (PDMMS) founded on an initial list of manifestations derived from an original content validation in a general population. Twenty-three items are grouped in four oblique factors with Cronbach's alpha ranging between 0.81 and 0.89. High scores on the scale are correlated with psychoactive drugs consumption, visits to health professionals and self-evaluations of psychological health.