Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

Natalie S Wagenbrenner,Bret W Butler,Brian K Lamb,Kyle S Shannon,Jason M Forthofer

doi:10.5194/acp-16-5229-2016

Abstract

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

Highlights

Researchers from multiple disciplines rely on routine forecasts from numerical weather prediction (NWP) models to drive transport and dispersion models, conduct wind assessments for wind energy projects, and predict the spread of wildfires
The goals of this work were to (1) investigate the accuracy of NWP model near-surface wind predictions in complex terrain on spatial scales relevant for processes driven by local surface winds, such as wildland fire behavior and (2) assess the ability of a mass-consistent wind model to improve these predictions through dynamic downscaling
Results showed that the NWP models captured the important large-scale flow features around BSB under most conditions, but were not capable of predicting the high spatial variability in the observed winds on and around the butte induced by mechanical effects of the terrain and local surface heating and cooling

Summary

Introduction

Researchers from multiple disciplines rely on routine forecasts from numerical weather prediction (NWP) models to drive transport and dispersion models, conduct wind assessments for wind energy projects, and predict the spread of wildfires. These applications require fine-scale, near-surface wind predictions in regions where rugged terrain and vegetation have a significant effect on the local flow field. Numerous operational mesoscale NWP model forecast products are available in real-time, such as those provided by National Centers for Environmental Prediction (NCEP) Access to these output products is facilitated by automated archiving and distribution systems such as the National Operational Model Archive and Distribution System (NOMADS). The spatial resolution of the system of interest (e.g., wildland fire spread) is much finer than that of the NWP model output

Objectives

Results

Conclusion