Long-term changes in North American monsoon (NAM) precipitation intensity in the southwestern United States are evaluated through the use of convective-permitting model simulations of objectively identified severe weather events during “historical past” (1950–70) and “present day” (1991–2010) periods. Severe weather events are the days on which the highest atmospheric instability and moisture occur within a long-term regional climate simulation. Simulations of severe weather event days are performed with convective-permitting (2.5 km) grid spacing, and these simulations are compared with available observed precipitation data to evaluate the model performance and to verify any statistically significant model-simulated trends in precipitation. Statistical evaluation of precipitation extremes is performed using a peaks-over-threshold approach with a generalized Pareto distribution. A statistically significant long-term increase in atmospheric moisture and instability is associated with an increase in extreme monsoon precipitation in observations and simulations of severe weather events, corresponding to similar behavior in station-based precipitation observations in the Southwest. Precipitation is becoming more intense within the context of the diurnal cycle of convection. The largest modeled increases in extreme-event precipitation occur in central and southwestern Arizona, where mesoscale convective systems account for a majority of monsoon precipitation and where relatively large modeled increases in precipitable water occur. Therefore, it is concluded that a more favorable thermodynamic environment in the southwestern United States is facilitating stronger organized monsoon convection during at least the last 20 years.

Capsule Summary An integrated, high resolution, data-driven regional modeling system has been recently developed for the Red Sea region and is being used for research and various environmental applications.

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, Lion (EvoLved Sign Momentum). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% zero-shot and 91.1% fine-tuning accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. The implementation of Lion is publicly available. 1 * Work done as a student researcher at Google Brain. † Work done while at Google.

This study analyses the connection between extreme rainfall events in Jeddah, Saudi Arabia, and synoptic-scale weather patterns over the Arabian Peninsula.Mean rainfall follows a decreasing trend; however, the number of rainy days has increased. Interestingly, extreme rainfall is becoming less frequent but shows an increased intensity. Here we utilize self-organizing maps (SOMs) to identify the weather patterns of the most intense rainy days and the synoptic systems causing extreme rainfall in the Jeddah region. Three main weather patterns that cause heavy rainfall events over Jeddah during the cooler months (November-April) are identified, all reflect tropical-extratropical interactions. Extreme events in the early period (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998) are characterized by a stronger tropical influence and local precipitation patterns, while a stronger extratropical forcing and higher extreme rainfall amounts are spotted in the late period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). Our results suggest that in recent decades, the mechanism causing extreme rainfall over the city of Jeddah has shifted toward a weather regime with stronger extratropical influence.