{"title":"Proxy observations of surface wind from a globally distributed network of wave buoys","authors":"Ciara Dorsay, Galen Egan, Isabel Houghton, Christie Hegermiller, Pieter B. Smit","doi":"10.1175/jtech-d-23-0044.1","DOIUrl":null,"url":null,"abstract":"Abstract In the equilibrium range of the wave spectrum’s high frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021–2022) when compared with collocated observations derived from satellites (yielding over 20000 collocations) and reanalysis data. We consider physics motivated parameterizations (based on frequency −4 universal tail assumption), inverse modelling (estimate wind speed from spectral energy balance), and a data driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test-data reveals comparable performance across methods with generally order 1 m/s root-mean-square-difference with satellite observations.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"42 19","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/jtech-d-23-0044.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract In the equilibrium range of the wave spectrum’s high frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021–2022) when compared with collocated observations derived from satellites (yielding over 20000 collocations) and reanalysis data. We consider physics motivated parameterizations (based on frequency −4 universal tail assumption), inverse modelling (estimate wind speed from spectral energy balance), and a data driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test-data reveals comparable performance across methods with generally order 1 m/s root-mean-square-difference with satellite observations.
期刊介绍:
The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.