Identification of slushflow situations from regional weather models

Slushflows are known phenomena that cause significant problems for settlements and infrastructure in Norway. Even though single events in the same location are rather rare compared to avalanches, slushflows do occur annually on a national scale. Both intensive snowmelt events as well as high amounts of rain on the snow cover can cause slushflows during the whole winter season. In recent years eight fatalities and widespread problems for infrastructure in Norway have increased the focus on slushflows. Early warning criteria based on readily available meteorological, hydrological and snow data need to be identified to allow a nationwide monitoring of potentially critical situations and corresponding locations that might lead to slushflow events. Earlier work focused on input data from meteorological stations. These stations are often located at sea level and give little information on the meteorological conditions in the release and drainage areas in the mountains. During the last decade, regional weather models have been developed that deliver weather prognosis every hour with up to 4 km grid resolution. In Norway, observed precipitation and temperature are interpolated to a one-kilometre grid and used to model snow conditions and snowmelt. This study aims at analysing the available data to identify critical meteorological elements and their thresholds for the release of slushflows. Examples from recent years will be studied also taking into account the development of the snow cover prior to the slushflow events. The results indicate that the available data has a promising ability to identify critical situations on a regional level

A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting

Good understanding of micro-scale urban-wind phenomena is needed for optimizing power generation capabilities of building-integrated wind turbines and for safety of futuristic urban transport involving drones. The current work involves development of a multiscale methodology for obtaining wind fields in an urban landscape. The multi-scale methodology involves coupling three models operating on different scales namely an operational meso-scale numerical weather prediction model HARMONIE, a micro-scale model that captures terrain- induced wind influence and a super-micro scale Computational Fluid Dynamics model using large eddy simulation and RANS model to capture the building-induced wind flow. Here, we present a comparison of the wind velocity predicted by two different turbulence models (LES and RANS) that are operating at the finest scale with the measured experiment data for a realistic case of flow in vicinity of the Oslo university hospital. The reasons behind the observed better prediction of LES model are outlined, and use of such models is advocated to improve accuracy

A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting

Good understanding of micro-scale urban-wind phenomena is needed for optimizing power generation capabilities of building-integrated wind turbines and for safety of futuristic urban transport involving drones. The current work involves development of a multiscale methodology for obtaining wind fields in an urban landscape. The multi-scale methodology involves coupling three models operating on different scales namely an operational meso-scale numerical weather prediction model HARMONIE, a micro-scale model that captures terrain- induced wind influence and a super-micro scale Computational Fluid Dynamics model using large eddy simulation and RANS model to capture the building-induced wind flow. Here, we present a comparison of the wind velocity predicted by two different turbulence models (LES and RANS) that are operating at the finest scale with the measured experiment data for a realistic case of flow in vicinity of the Oslo university hospital. The reasons behind the observed better prediction of LES model are outlined, and use of such models is advocated to improve accuracy.publishedVersio

A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting

Good understanding of micro-scale urban-wind phenomena is needed for optimizing power generation capabilities of building-integrated wind turbines and for safety of futuristic urban transport involving drones. The current work involves development of a multiscale methodology for obtaining wind fields in an urban landscape. The multi-scale methodology involves coupling three models operating on different scales namely an operational meso-scale numerical weather prediction model HARMONIE, a micro-scale model that captures terrain- induced wind influence and a super-micro scale Computational Fluid Dynamics model using large eddy simulation and RANS model to capture the building-induced wind flow. Here, we present a comparison of the wind velocity predicted by two different turbulence models (LES and RANS) that are operating at the finest scale with the measured experiment data for a realistic case of flow in vicinity of the Oslo university hospital. The reasons behind the observed better prediction of LES model are outlined, and use of such models is advocated to improve accuracy

TOWARDS UNDERSTANDING WIND IMPACT FOR DRONE OPERATIONS: A COMPARISON OF WIND MODELS OPERATING ON DIFFERENT SCALES IN A NESTED MULTISCALE SET-UP.

The application of Unmanned Aircraft Systems (UAS) in health services is increasing, with a large variety of objectives: delivering medicines and vaccines, transporting blood samples and providing care technology in emergency situations. However, for use in emergency medical purposes, the expectations are a drone should be available at most times. Severe wind conditions are considered to be one of the prime factor that can hamper this expected drone availability. Most of these drone operations are expected to be linked to urban hospitals and understanding urban micro-scale weather patterns are important. The current work tries to develop a methodology for obtaining wind fields in an urban landscape. The multi-scale methodology involves coupling three models operating on different scales namely an operational meso-scale numerical weather prediction model HARMONIE, a micro-scale model that captures terrain-induced wind influence and a super-micro scale Computational Fluid Dynamics code to capture building-induced wind influence. Existence of a large variation in the spatio-temporal scales in an atmospheric flow necessitates such a coupling between different models each of which handles a particular range of scales. In this article, we describe the multi-scale methodology and present a qualitative comparison of the wind velocity predicted by different numerical models with the measured experiment data and then explain the potential of the tool for drone operations.publishedVersio