Observation System Experiments with the Hourly-Updating Rapid Refresh (RAP) Model Using GSI Hybrid Ensemble/Variational Data Assimilation
Observation System Experiments with the Hourly-Updating Rapid Refresh (RAP) Model Using GSI Hybrid Ensemble/Variational Data Assimilation
复制标题
使用 GSI 混合集成/变分数据同化的每小时更新快速刷新 (RAP) 模型的观测系统实验
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Stanley G. Benjamin
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文献类型：
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作者：
Eric P. James;Stanley G. Benjamin
使用每小时更新的快速更新（RAP）数值天气预报（NWP）同化预报系统，在三个季节进行了一组观测系统试验（OSE），确定了北美观测系统的各个组成部分对3-12小时RAP预报的重要性。飞机观测成为对风、相对湿度（RH）和温度预报影响最大的观测类型，允许对流层和平流层下部的6小时预报误差减少15%-30%。主要的积极影响也可以从rawinsondes，GOES卫星云观测和地面观测中看到，GPS可降水量（PW）观测，卫星大气运动矢量（AMV）和雷达反射率观测的影响较小，但仍然很大。一个单独的实验表明，飞机相关的相对湿度预报的改善增加了50%，特别是由于增加了飞机湿度观测。此外，从R…
AbstractA set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en r...