%0 Journal Article %T Impact of Iranian permanent GPS network precipitable water estimates on numerical weather prediction %J Earth Observation and Geomatics Engineering %I University of Tehran %Z 2588-4352 %A Sam-Khaniani, Ali %A Azadi, Majid %A Zakeri, Zeinab %D 2017 %\ 12/01/2017 %V 1 %N 2 %P 100-111 %! Impact of Iranian permanent GPS network precipitable water estimates on numerical weather prediction %K 4DVAR assimilation %K WRF %K GPS PWV %K Surface observations %K precipitation %R 10.22059/eoge.2017.243645.1013 %X The aim of this study is to assess the impact of continuous and precise ground-based GPS water vaporestimates as a by-product of Iranian Permanent GPS Network (IPGN) geodetic data processing, togetherwith conventional surface and upper air meteorological data on the short range prediction of rainfall andsurface moisture fields, including 2 m relative humidity and Precipitable Water Vapor (PWV) over northof Iran. The Weather Research and Forecasting (WRF) model and its Four-Dimentional Variational DataAssimilation (4DVAR) system is used to determine the impact of data assimilation on simulation of threeheavy rainfall cases that occurred over the northern part of Iran. All three rainfall cases considered in thisstudy are associated with a shallow and cold high pressure located over Russia that extends towards thesouthern Caspian Sea. The results of numerical experiments showed that the assimilation of ground-basedGPS PWV data, on average, improves simulation of precipitation, PWV and near surface relativehumidity, even though the skill declines after 24-h simulation. It is found that inclusion of GPS PWVimproved the predicted accumulated precipitation in day-1 of the model simulations for February andNovember cases up to 7 percent while there was almost no positive impact in September case. Resultssuggest that incorporation of observations in initial conditions of the WRF gives generally a slightimprovement in 2 m relative humidity forecasts when compared with the control experiment withoutassimilation. Assimilation of GPS PWV in February and September cases reduces, on average, 0.8 mm theMean Absolute Error (MAE) of the PWV model during 12-h forecast period. Overall, best results in termsof MAEs were achieved when GPS water vapor estimations were used along with conventional surfaceand upper air radiosonde data. %U https://eoge.ut.ac.ir/article_64290_d3a0937125c8ba40328602a156d7b62e.pdf