Review of Assimilating Spaceborne Global Navigation Satellite System Remote Sensing Data for Tropical Cyclone Forecasting

Review of Assimilating Spaceborne Global Navigation Satellite System Remote Sensing Data for Tropical Cyclone Forecasting

Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spacebo...

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Bibliographic Details
Main Authors: Weihua Bai, Guanyi Wang, Feixiong Huang, Yueqiang Sun, Qifei Du, Junming Xia, Xianyi Wang, Xiangguang Meng, Peng Hu, Cong Yin, Guangyuan Tan, Ruhan Wu
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Remote Sensing
Subjects:
GNSS RO
GNSS-R
tropical cyclone
forecast
Online Access:https://www.mdpi.com/2072-4292/17/1/118
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Summary:Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting.
ISSN:2072-4292

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