Statistics – Computation
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsm51a1772w&link_type=abstract
American Geophysical Union, Fall Meeting 2010, abstract #SM51A-1772
Statistics
Computation
[0550] Computational Geophysics / Model Verification And Validation, [1910] Informatics / Data Assimilation, Integration And Fusion, [2447] Ionosphere / Modeling And Forecasting, [3225] Mathematical Geophysics / Numerical Approximations And Analysis
Scientific paper
One of main objectives established in 2000 for the development of a global data assimilation model for the Earth’s ionosphere was to enable the forecast of ionospheric electron and ion densities. Following the exciting development of Global Assimilative Ionospheric Model (GAIM, also known as the Global Assimilation of Ionospheric Measurements) by two teams, the Utah State University team and the University of Southern California and the Jet Propulsion Laboratory team, the goal of forecasting ionospheric electron density has yet to be reached. At the University of Southern California and the Jet Propulsion Laboratory, we have made substantial efforts toward the forecasting of ionospheric conditions. A key component of our efforts is the determination of the driving forces for the ionospheric dynamics using the 4DVAR data assimilation approach. It is well-known that the changes in the electron and ion densities in the Earth’s ionosphere are strongly influenced by the variations of the solar radiation, the geo-electrical field, the neutral gas densities and thermospheric wind velocity. These environmental variables are referred to as the driving forces in an ionospheric model. In early version of the GAIM implementation, the values of these driving forces are taken from climatological models often indexed simply by geomagnetic index AP and solar irradiance index F10.7. Although these crude estimates of the values of these driving forces are sufficient in providing a prior estimate for electron density for approaches based on Kalman filter to produce reasonably good ionospheric now-cast, these values are not sufficient for forecast of ionospheric densities. A 4DVAR version of the USC/JPL GAIM was developed to estimate the Earth’s ionospheric driving forces such as the production rate, the ExB drift velocity and the horizontal neutral wind speed. The implementation uses the approach of adjoint equation to efficiently evaluate the gradient vector of the 4DVAR optimization criterion. The same approach also allows us to simultaneously estimate the driving forces and the electron density. The 4DVAR implementation relies on an intermittent assimilation cycle to process measurement data and to produce forecasts. Sensitivity studies of the ionospheric observables to the driving forces are performed by the USC/JPL team. We have also investigated the feasibility of the estimation of the ionospheric drivers through a series of Observation System Simulation Experiments (OSSE). Our simulation results indicate that when persistence in time in the sun fixed frame is a valid assumption, the 4DVAR approach can be effectively used to forecast the ionospheric conditions. In this presentation, we present our implementation of the 4DVAR model and the scheduling of data assimulation cycles. We shall also present the results of our sensitivity study, as well as, the results of the OSSEs.
Akopian Vardan
Mannucci Anthony J.
Pi Xiaoqing
Usc/Jpl Gaim Team
Wang Chenjie
No associations
LandOfFree
Forecasting Ionospheric Conditions with 4DVAR Assimilation Model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Forecasting Ionospheric Conditions with 4DVAR Assimilation Model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Forecasting Ionospheric Conditions with 4DVAR Assimilation Model will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1473338