An ERA5 based local modelling of weighted mean temperature over hilly region in India for improved spatiotemporal analysis of extreme weather event using GNSS PWV

Abstract

Precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) offers the benefits of high spatiotemporal resolution logging-consistency and all-weather operability. Estimation of water vapor weighted mean temperature (Tm) is a crucial part and a major error source in extracting GNSS PWV from atmospheric delay. Empirical modelling of Tm is a convenient approach, especially in cases where, the meteorology community suffers from inefficient and inconsistent logging of temperature and humidity profiles etc., in the existing meteorological observing systems, particularly for harsh weather conditions. In a first, a novel site specific model (Tm-SSM) is developed for the atmospheric weighted mean temperature (Tm) for 13 CORS (continuously operating reference stations) in the Survey of India (SOI) network, recently established in the hilly state of Uttarakhand (UK) using the ERA5 reanalysis data from 2016 to 2018. GNSS observations at 10 CORS are then used to observe the spatiotemporal characteristics of PWV from Tm-SSM during the heavy rainfall event in October 2021. The newly developed Tm-SSM performs better with the mean bias error (MBE) and root mean square error (RMSE) of 0.498 and 2.9 K, respectively, when compared with other globally accepted models. The Tm-SSM PWV estimates are well correlated with the precipitation data from AWS stations. The newly developed Tm-SSM is well conditioned and highly stable, with the mean stability indicator value as low as 1.043%. Further, a close relationship can be observed from the spatiotemporal characteristics of GNSS PWV during heavy rainfall event, with the in-situ precipitation measurements. It is apparent from the results that PWV steadily builds up before the arrival of rain, with sharp peaks in PWV observed just before rainfall event. It then, follows a downward slope through which it decreases to a stable value of around 30 mm when the rainfall ceases. The fluctuations in the PWV from Tm-SSM and the passage of the severe mesoscale convective system in a complex terrain such as UK were shown to have high spatial and temporal correlation, which is significant for the monitoring and forecasting such extreme weather. The findings imply that GNSS PWV represents the compendious signature of the water vapor dynamics during severe weather, demonstrating the potential to improve the early detection and sensing of severe weather.