Current gust forecasting techniques, developments and challenges

Abstract

Abstract.Gusts represent the component of wind most likely to be associated with serious hazards and structural damage, representing short-lived extremes within the spectrum of wind variation. Of interest both for short range forecasting and for climatological and risk studies, this is also reflected in the variety of methods used to predict gusts based on various static and dynamical factors of the landscape and atmosphere. The evolution of Numerical Weather Prediction (NWP) models has delivered huge benefits from increasingly accurate forecasts of mean near-surface wind, with which gusts broadly scale. Techniques for forecasting gusts rely on parametrizations based on a physical understanding of boundary layer turbulence, applied to NWP model fields, or statistical models and machine learning approaches trained using observations, each of which brings advantages and disadvantages.Major shifts in the nature of the information available from NWP models are underway with the advent of ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields. This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts on the performance of gust forecasting using current methods. The transition to probabilistic prediction using ensembles at the regional level means that considerations such as these must also be carried through to the aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 4.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

Short‐Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

“…Indeed, WS 950 or WS 850 is included as a predictor in the majority of models of both occurrence and magnitude at all stations and in both seasons. Use of sustained wind speeds in the wind gust forecast models is consistent with past research Sallis et al, 2011;Sheridan, 2018), and past operational analyses, where the maximum sustained winds (usually with averaging periods up to 1 min) at 10-m associated with tropical cyclones were historically estimated as 80% of wind speeds measured at 850 hPa and 85% of those measured at 450 m AGL (Sparks, 2003). The stepwise procedure for predictor selection also frequently retains the low-level vertical temperature gradient (dT at 900 hPa, or the lowest level above ground for KDEN, 750 hPa).…”

Current Challenges in Orographic Flow Dynamics: Turbulent Exchange Due to Low-Level Gravity-Wave Processes

“…A comparison of Figure 4d with Figure 4b highlights the difference in peak stress (or equivalently, u*) values. This is important for the parametrised diagnosis of gusts; a parametrisation designed as a "boost" to the mean wind in coarse models based on a Monin-Obukhov approach will egregiously overplay gusts at very high resolution where in any case gusts already begin to be resolved [65].…”

Meteorologically Stratified Gust Factors for Forecasting Peak Wind Gusts across the United States

“…Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. Existing methods, recently summarized by Sheridan (2018), include physically based models involving postprocessing of numerical weather prediction output variables in light of physical reasoning concerning boundary layer turbulence, and statistical, data-driven methods employing extreme value statistics or empirical approaches. Unfortunately, the development and verification of gust forecast models is hindered by sampling and reporting protocols associated with both operational and archived wind observations (Harris and Kahl 2017).…”