Evaluation of IWV from the numerical weather prediction WRF model with PPP GNSS processing for Bulgaria

Abstract

Abstract. Global Navigation Satellite Systems (GNSS) meteorology is an established operational service providing hourly updated GNSS tropospheric products to the National Meteorologic Services (NMS) in Europe. In the last decade through the ground-based GNSS network densification and new processing strategies like Precise Point Positioning (PPP) it has become possible to obtain sub-hourly tropospheric products for monitoring severe weather events. In this work one year (JanuaryDecember 2013) of sub-hourly GNSS tropospheric products (Zenith Total Delay) are computed using the PPP strategy for 5 seven stations in Bulgaria. In order to take advantage of the sub-hourly GNSS data to derive Integrated Water Vapour (IWV) surface pressure and temperature with similar temporal resolution is required. As the surface observations are on 3 hourly basis the first step is to compare the surface pressure and temperature from numerical weather prediction model Weather Forecasting and Research (WRF) with observations at three synoptic stations in Bulgaria. The mean difference between the two data-sets for 1) surface pressure is less than 0.5 hPa and the correlation is over 0.989, 2) temperature the largest mean difference is Between 00 and 01 UTC the GNSS IWV tends to be underestimate IWV which is likely due to the processing window used.

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The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function

“…It has the potential to forecast severe weather phenomena [2][3][4] and examine the effects of climate change [5,6]. Previous studies [3,[7][8][9][10] have shown that there will be severe rainstorms in the downward trend process after GNSS-PWV reaches its peak value. Benevides et al [11] proposed that the accuracy of weather forecasting could be improved after analyzing 3D distribution variations of PWV [12][13][14][15][16].…”

Estimation of integrated water vapor derived from Global Navigation Satellite System observations over Central-Western Argentina (2015–2018). Validation and usefulness for the understanding of regional precipitation events

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