GPS Ionospheric mapping and tomography: A case of study in a geomagnetic storm

The ionosphere has been normally detected by traditional instruments, such as ionosonde, scatter radars, topside sounders onboard satellites and in situ rocket. However, most instruments are expensive and also restricted to either the bottomside ionosphere or the lower part of the topside ionosphere (usually lower than 800 km), such as ground based radar measurements. Nowadays, GPS satellites in high altitude orbits (~20,200 km) are capable of providing details on the structure of the entire ionosphere, even the plasmasphere. In this paper, a Regional Ionospheric Mapping and Tomography (RIMT) tool was developed, which can be used to retrieve 2-D TEC and 3-D ionospheric electron density profiles using ground-based or space-borne GPS measurements. Some results are presented from the RIMT tool using regional GPS networks in South Korea and validated using the independent ionosonde. GPS can provide time-varying ionospheric profiles and information at any specified grid related to ionospheric activities and states, including the electron density response at the F2-layer peak (the NmF2) during geomagnetic storms.Comment: Proceeding of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 24-29 July, 2011, Vancouver, Canad

Earth's surface fluid variations and deformations from GPS and GRACE in global warming

Global warming is affecting our Earth's environment. For example, sea level is rising with thermal expansion of water and fresh water input from the melting of continental ice sheets due to human-induced global warming. However, observing and modeling Earth's surface change has larger uncertainties in the changing rate and the scale and distribution of impacts due to the lack of direct measurements. Nowadays, the Earth observation from space provides a unique opportunity to monitor surface mass transfer and deformations related to climate change, particularly the global positioning system (GPS) and the Gravity Recovery and Climate Experiment (GRACE) with capability of estimating global land and ocean water mass. In this paper, the Earth's surface fluid variations and deformations are derived and analyzed from global GPS and GRACE measurements. The fluids loading deformation and its interaction with Earth system, e.g., Earth Rotation, are further presented and discussed.Comment: Proceeding of Geoinformatics, IEEE Geoscience and Remote Sensing Society (GRSS), June 24-26, 2011, Shanghai, Chin

Water storage changes and balances in Africa observed by GRACE and hydrologic models

AbstractContinental water storage plays a major role in Earth's climate system. However, temporal and spatial variations of continental water are poorly known, particularly in Africa. Gravity Recovery and Climate Experiment (GRACE) satellite mission provides an opportunity to estimate terrestrial water storage (TWS) variations at both continental and river-basin scales. In this paper, seasonal and secular variations of TWS within Africa for the period from January 2003 to July 2013 are assessed using monthly GRACE coefficients from three processing centers (Centre for Space Research, the German Research Centre for Geosciences, and NASA's Jet Propulsion Laboratory). Monthly grids from Global Land Data Assimilation System (GLDAS)-1 and from the Tropical Rainfall Measuring Mission (TRMM)-3B43 models are also used in order to understand the reasons of increasing or decreasing water storage. Results from GRACE processing centers show similar TWS estimates at seasonal timescales with some differences concerning inter-annual trend variations. The largest annual signals of GRACE TWS are observed in Zambezi and Okavango River basins and in Volta River Basin. An increasing trend of 11.60 mm/a is found in Zambezi River Basin and of 9 mm/a in Volta River Basin. A phase shift is found between rainfall and GRACE TWS (GRACE TWS is preceded by rainfall) by 2–3 months in parts of south central Africa. Comparing GLDAS rainfall with TRMM model, it is found that GLDAS has a dry bias from TRMM model