Impact of Iranian permanent GPS network precipitable water estimates on numerical weather prediction

Abstract

<span>The aim of this study is to assess the impact of continuous and precise ground-based GPS water vapor<br /><span>estimates as a by-product of Iranian Permanent GPS Network (IPGN) geodetic data processing, together<br /><span>with conventional surface and upper air meteorological data on the short range prediction of rainfall and<br /><span>surface moisture fields, including 2 m relative humidity and Precipitable Water Vapor (PWV) over north<br /><span>of Iran. The Weather Research and Forecasting (WRF) model and its Four-Dimentional Variational Data<br /><span>Assimilation (4DVAR) system is used to determine the impact of data assimilation on simulation of three<br /><span>heavy rainfall cases that occurred over the northern part of Iran. All three rainfall cases considered in this<br /><span>study are associated with a shallow and cold high pressure located over Russia that extends towards the<br /><span>southern Caspian Sea. The results of numerical experiments showed that the assimilation of ground-based<br /><span>GPS PWV data, on average, improves simulation of precipitation, PWV and near surface relative<br /><span>humidity, even though the skill declines after 24-h simulation. It is found that inclusion of GPS PWV<br /><span>improved the predicted accumulated precipitation in day-1 of the model simulations for February and<br /><span>November cases up to 7 percent while there was almost no positive impact in September case. Results<br /><span>suggest that incorporation of observations in initial conditions of the WRF gives generally a slight<br /><span>improvement in 2 m relative humidity forecasts when compared with the control experiment without<br /><span>assimilation. Assimilation of GPS PWV in February and September cases reduces, on average, 0.8 mm the<br /><span>Mean Absolute Error (MAE) of the PWV model during 12-h forecast period. Overall, best results in terms<br /><span>of MAEs were achieved when GPS water vapor estimations were used along with conventional surface<br /><span>and upper air radiosonde data.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span>