海洋开发与管理2023，Vol.40Issue(11)：93-101,9.
日照市海雾天气的特征分析与预报
Characteristic Analysis and Forecast of Sea Fog Weather in Rizhao City
摘要
Abstract
Large quantity of observational data from ground and sounding meteorological obser-vation data,ocean station observation data,ERA reanalysis data are used to count the sea fog process that occurred during April to July in Rizhao City from 1988 to 2019 altogether 32 years in this paper.The synoptic characteristics of the sea fog and their formation mechanism are statistically analyzed,and furthermore the main meteorological elements affecting the for-mation of sea fog in Rizhao area and their combined effects are put forward.The results indi-cate that sea fog occur in Rizhao area is most likely under the control of several weather condi-tions,such as the west of transition high to sea,the Pacific subtropical high ridge or the front of low or trough of the eastern China in spring and summer.Under the control of these synop-tic characteristics,the direction and speed of wind,dew-point temperature,atmospheric tem-perature,SST,stratification stability of these hydrometeorological elements play an important role in the formation of sea fog.On these basis,the method of discriminance analy-sis is used to establish of discriminant equation for sea fog forecast.Through the forecast tes-ting,the statistical forecasting method of sea fog has a great application,the forecast accuracy is between 88%and 94%.It can be used for short-term forecast of sea fog characteristics in Rizhao City,providing services for industry,agriculture,fishery production,sea and land transportation.关键词
海雾/天气形势/水文气象要素/特征分析/Fisher二级判别分析Key words
Sea fog/Weather elements/Hydrometeorological elements/Characteristic analysis/Fisher two-level discriminant analysis分类
海洋科学引用本文复制引用
郑智勇,丁森,王珍珍,刘迎迎,冯立达,侯继灵,黄帅,郑聪聪..日照市海雾天气的特征分析与预报[J].海洋开发与管理,2023,40(11):93-101,9.基金项目
国家重点研发计划"海洋环境安全保障"专项"专题预警报产品关键技术研究"(2018YFC1407000) （2018YFC1407000）
北海局海洋科技项目"北海区近岸海陆风效应特征分析及其近海精细化数值预报研究"(202107) （202107）
山东省海洋生态环境与防灾减灾重点实验室开放基金资助项目"基于向量基归(SVR)的机器学习方法对山东近海海浪波高的预测研究"(202210). （SVR）
