基于海洋浮标GNSS PWV的海洋降水量短时预测  

Short-term prediction of marine precipitation based on GNSS PWV of marine buoys

作　　者：龚非非 GONG Feifei(Shanghai Jiaotong Dahaike Testing Technology Company Limited,Shanghai 200231,China)

机构地区：[1]上海交大海科检测技术有限公司,上海200231

出　　处：《北京测绘》2025年第1期8-13,共6页Beijing Surveying and Mapping

摘　　要：本文基于18天的全球导航卫星系统(GNSS)浮标观测数据,对大气中的可降水汽含量(PWV进行了反演,并进行了精度验证。探讨了GNSS PWV与实际降水量之间的关系,引入了随机森林方法,基于PWV数据对降水期间的降水量进行了预测。结果表明,浮标GNSS PWV与固定站GNSS PWV之间的差异较小,平均差值小于1 mm,标准差为0.4 mm,表明基于GNSS浮标进行PWV反演的精度较高。分析GNSS PWV与实际降水量之间的关系发现,在降雨发生前,PWV值会逐渐增加,达到峰值后开始降雨;降雨期间,PWV呈现波动状态;降雨结束后,PWV值明显下降,逐渐恢复到正常水平。在集中降水期间PWV与降水量的相关性高达65.5%。使用随机森林算法构建降水量预测模型,模型预测的结果与实际观测结果高度一致,在降水量超过0.5 mm时,预测降水量的相对误差小于25%。利用控制变量法对输入特征的重要性进行分析,结果显示PWV对于海洋降水量预测的重要性远大于相对湿度。Based on 18-day observation data of global navigation satellite system(GNSS)buoys,the precipitable water vapor(PWV)in the atmosphere was retrieved,and the accuracy was verified.The relationship between GNSS PWV and actual precipitation was explored by introducing a random forest method,and the precipitation during rainfall was predicted based on PWV data.The results show that the difference between the GNSS PWV of buoys and the GNSS PWV of fixed stations is small.The average difference is less than 1 mm,and the standard deviation is 0.4 mm,which indicates that PWV inversion based on the GNSS buoys has high accuracy.The analysis of the relationship between GNSS PWV and actual precipitation shows that the PWV value will gradually increase before the occurrence of rainfall,and rainfall starts after the PWV value reaches the peak.PWV fluctuates during rainfall.After the end of the rainfall,the PWV value decreases significantly and gradually recovers to the normal level.The correlation between PWV and precipitation is as high as 65.5%during the concentrated rainfall period.The random forest algorithm is used to construct the precipitation prediction model,and the predicted results of the model are highly consistent with the actual observation results.When the precipitation exceeds 0.5 mm,the relative error of the predicted precipitation is less than 25%.The control variable method is used to analyze the importance of input characteristics,and the results show that PWV is much more important than relative humidity for marine precipitation prediction.

关 键 词：可降水汽含量(PWV) 全球导航卫星系统(GNSS)浮标 降水量 随机森林 

分 类 号：P228.4[天文地球—大地测量学与测量工程]