This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results show that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses. INDEX TERMS Power distribution, extreme events, machine learning, numerical weather predictions, power outage prediction.
The potential vorticity (PV) anomalies due to the intrusion of dry stratospheric air and those generated by the tropospheric diabatic latent heating are qualitatively analyzed for five Mediterranean tropical‐like cyclones (also known as Medicanes). Model simulations show the presence of an upper level PV streamer in the early stages of the cyclone, located on the left exit of a jet stream, and a middle‐low level PV anomaly generated by the convection developing around the low‐level vortex. In the mature stage, the upper level PV anomaly around the cyclone evolves differently for each case and appears somehow dependent on the lifetime. Only for the 2006 Medicane, the PV anomalies form an intense PV tower extending continuously from the lower troposphere to the lower stratosphere.
Predicting the trajectory and structure of Mediterranean tropical-like cyclones (MTLCs) has always been a challenge even within a few hours of verification time, given the inadequacy of numerical weather prediction (NWP) models to resolve the relatively small spatial scale of these systems. In particular, the event of 7-8 November 2014 was poorly predicted by operational NWP models which failed to reproduce the trajectory of the cyclone. Using a state-of-the-art storm-resolving model, we show that simulations with a grid spacing of approximately 1 km are able to reproduce the fine-scale structure of this MTLC. Simulations performed with grid spacing larger than 2.5 km fail to represent the features of the cyclone, while additional nested simulations with very high resolution (300 m) reveal the ability of the model to fully capture the internal structure of the cyclone. Thus, there is a noticeable convergence towards the observed trajectory of the cyclone with increasing resolution. Finally, a potential vorticity (PV) analysis highlights the mutual interaction between a PV streamer and a low-level PV maximum induced by convection. Only convection-resolving simulations, with a grid spacing smaller than 5 km, show a low-level maximum of PV which impacts the redistribution of PV at the higher atmospheric levels.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.