Statistical Validation of GNSS Radio Occultation data over Egypt

Gomaa, Hala Eldosoky; Mousa, Ashraf; Yousef, Mohamed; Mahrous, Ayman

doi:10.21608/abas.2024.223245.1024

Statistical Validation of GNSS Radio Occultation data over Egypt

Document Type : Original Article

Authors

¹ physics department, faculty of science, Helwan University

² National Research Institute of Astronomy and Geophysics (NRIAG)

³ Space Weather Monitoring Centre (SWMC), Faculty of Science, Helwan University

10.21608/abas.2024.223245.1024

Abstract

Radiosonde (RS) measurements of the upper atmosphere have long been the principal source of
data, with measurements spanning more than 60 years . RS have limited number of stations on
Egypt. Thus, it has poor spatial and temporal resolution, and This negatively affects
meteorological research, atmospheric observation, and weather forecasting studies over Egypt. It
is needed to find an additional technique characterized by the length of the available time series
as well as with the high vertical resolution.
Global Positioning System Radio Occultation (GPS RO) is the satellite technique received by a
low-Earth orbiting satellites to profile the Earth's atmosphere with high vertical resolution and
global coverage. This technique has been found to enhance weather forecasting and climate
monitoring. In this study, the GPS RO temperature and vapor pressure profiles of COSMIC
satellite mission over Egypt from 2007 to 2019 have been validated with those of the available
six Radiosonde stations (RS). GPS RO and RS profiles collocated by matching the position and
the time, The horizontal distance between the chosen RS stations and the GPS RO event is
within 150 km and the time window was 3 hours. The comparison was performed only on the
standard pressure level.
The main objective of this paper is to check the accuracy of the GPS RO at different
elevations. For this aim the differences between RS and GPS RO were sorted according to
pressure level in to 4 groups. It is found from the results that GPS RO profiles agree well with
RS data. The mean temperature bias for all stations equal (0.22) with standard deviation (1.83).
On the other hand, mean vapor pressure bias equal (-0.02) with standard deviation (0.12).
According to pressure level sorting, the change of temperature mean bias and standard deviation
are increasing with the pressure level decreasing.

Keywords