{"title":"Exploring the Value of a High-Precision Targeted Observation Strategy for Mobile Radiosonde Deployment","authors":"Isaac Arseneau, B. Ancell","doi":"10.1175/mwr-d-22-0352.1","DOIUrl":null,"url":null,"abstract":"\nEnsemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Monthly Weather Review","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/mwr-d-22-0352.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
期刊介绍:
Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.