On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

Abstract

Improving the accuracy of numerical weather predictions remains a challenging task. The absence of sufficiently detailed temporal and spatial real-time in-situ measurements poses a critical gap regarding the proper representation of atmospheric moisture fields, such as water vapor distribution, which are highly imperative for improving weather predictions accuracy. The estimated amount of the total vertically integrated water vapor (IWV), which can be derived from the attenuation of global positioning systems (GPS) signals, can support various atmospheric models at global, regional, and local scales. Currently, several existing atmospheric numerical models can estimate the IWV amount. However, they do not provide accurate results compared with in-situ measurements such as radiosondes. Here, we present a new strategy for assimilating 2D IWV regional maps estimations, derived from combined GPS and METEOSAT satellite imagery data, to improve Weather Research and Forecast (WRF) model predictions accuracy in Israel and surrounding areas. As opposed to previous studies, which used point measurements of IWV in the assimilation procedure, in the current study, we assimilate quasi-continuous 2D GPS IWV maps, combined with METEOSAT-11 data. Using the suggested methodology, our results indicate an improvement of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative standalone WRF, when both are compared to the radiosonde measured data near the Mediterranean coast. Moreover, significant improvements along the Jordan Rift Valley and Dead Sea Valley areas are obtained when compared to 2D IWV regional maps estimations. Improvements in these areas suggest the impact of the assimilated high resolution IWV maps, with initialization times which coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations, as the distance to the Mediterranean Sea shore, along with other features, dictates its arrival times.

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

“…As these radio signals propagate through the earth's atmospheric layers (mainly along the troposphere and ionosphere) they are significantly affected by the physical characteristics of these layers and, thus, the propagation speed is reduced [43]. The extent of the delay depends largely on the temperature, pressure, and water vapor distribution, which differ considerably in space and time [44][45][46][47][48][49]. In contrast to the nondispersive tropospheric interaction, the speed at which radio signals propagate at a particular height through the ionosphere is constrained by the free electron density concentration in surrounding areas [50], and the radio signal phase speed is essentially increased by the existence of free electrons.…”

Long‐term variability and trends of precipitable water vapour derived from GPS tropospheric path delays over the Eastern Mediterranean

“…In the past three decades, GNSS-derived PWV has been compared to radiosonde measurements, radiometers, sun photometers and reanalysis products which resulted in an RMSE ranging from 1 to 3 mm, thus ensuring its importance as an atmospheric remote sensing platform (Li et al, 2003;Van Baelen et al, 2005;Wang et al, 2007;Pérez-Ramírez et al, 2014;Wang et al, 2017a). Furthermore, Leontiev et al (2021) showed that assimilating the GNSS-derived PWV maps into modern weather numerical prediction models (e.g., WRF) lowered the RMSE by more than 30% when comparing to radiosonde measurements.…”

“…Furthermore, Leontiev et al . (2021) showed that assimilating the GNSS‐derived PWV maps into modern weather numerical prediction models (e.g., WRF) lowered the RMSE by more than 30% when comparing to radiosonde measurements.…”

Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model‐Derived Wet Delay Corrections

“…Some previous studies also have proven that assimilation of external water vapor data, for example, GNSS PWV and Interferometric synthetic aperture radar (InSAR) PWV, into the WRF model can improve the WRF model's performance in forecasting PWV (Leontiev et al, 2020;Mateus & Miranda, 2022;Mateus et al, 2021;Xiong et al, 2020). For example, Leontiev et al (2020) assimilated PWV data from the Global Positioning System (GPS) PWV maps augmented by the Meteosat satellite imagery data into the WRF model to improve its PWV forecasting accuracy in Israel and neighboring areas. Their results indicated that the WRF model can forecast PWV with a root mean square error (RMSE) of 2.5-2.6 kg/m 2 for the forecast length of 3-12 hr.…”