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

Document Type: Original Article

Authors

1 Civil Engineering Department, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran

2 Atmospheric Science and Meteorological Research Center, Tehran, Iran

3 I.R. Iran Meteorological Organization, P.O. Box 13185-461, Tehran, Iran

Abstract

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

Keywords


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